This report is meant to help explore the results of the derfinder(Collado-Torres, Frazee, Jaffe, and Leek, 2014) package and was generated using regionReport(Collado-Torres, Jaffe, and Leek, 2014) package. While the report is rich, it is meant to just start the exploration of the results and exemplify some of the code used to do so. You will most likely need a more in-depth analysis for your specific data set.
Most plots were made with using ggplot2(Wickham, 2009).
## Loading required package: nlme
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## Attaching package: 'nlme'
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## The following object is masked from 'package:IRanges':
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## collapse
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## This is mgcv 1.8-3. For overview type 'help("mgcv-package")'.
## GitHub
library('derfinder')
## Working behind the scenes
# library('knitcitations')
# library('rmarkdown')
# library('knitrBootstrap')
#### Code setup
## For ggplot
tmp <- fullRegions
names(tmp) <- seq_len(length(tmp))
regions.df <- as.data.frame(tmp)
regions.df$width <- width(tmp)
rm(tmp)
nulls.df <- as.data.frame(fullNullSummary)
## Special subsets: need at least 3 points for a density plot
keepChr <- table(regions.df$seqnames) > 2
regions.df.plot <- subset(regions.df, seqnames %in% names(keepChr[keepChr]))
if(hasSig) {
## Keep only those sig
regions.df.sig <- regions.df[idx.sig, ]
keepChr <- table(regions.df.sig$seqnames) > 2
regions.df.sig <- subset(regions.df.sig, seqnames %in% names(keepChr[keepChr]))
if(nrow(regions.df.sig) > 0) {
## If there's any sig, keep those with finite areas
if(hasArea) {
finite.area.sig <- which(is.finite(regions.df.sig$area))
regions.df.sig.area <- regions.df.sig[finite.area.sig, ]
keepChr <- table(regions.df.sig.area$seqnames) > 2
regions.df.sig.area <- subset(regions.df.sig.area, seqnames %in%
names(keepChr[keepChr]))
## Save the info
hasArea <- (nrow(regions.df.sig.area) > 0)
}
} else {
hasSig <- hasArea <- FALSE
}
}
## Get chr lengths
if(hg19) {
data(hg19Ideogram, package = 'biovizBase')
seqlengths(fullRegions) <- seqlengths(hg19Ideogram)[mapSeqlevels(names(seqlengths(fullRegions)),
'UCSC')]
}
## Find which chrs are present in the data set
chrs <- levels(seqnames(fullRegions))
## Subset the fullCoverage data in case that a subset was used
colsubset <- optionsStats$colsubset
if(!is.null(fullCov) & !is.null(colsubset)) {
fullCov <- lapply(fullCov, function(x) { x[, colsubset] })
}
## Get region coverage for the top regions
if(nBestRegions > 0) {
if(packageVersion('derfinder') >= '0.0.60') {
regionCoverage <- getRegionCoverage(fullCov = fullCov,
regions = fullRegions[seq_len(nBestRegions)],
chrsStyle = chrsStyle, species = species,
currentStyle = currentStyle, verbose = FALSE)
} else {
regionCoverage <- getRegionCoverage(fullCov = fullCov,
regions = fullRegions[seq_len(nBestRegions)],
verbose = FALSE)
}
save(regionCoverage, file=file.path(workingDir, 'regionCoverage.Rdata'))
}
## Graphical setup: transcription database
if(hg19 & is.null(txdb)) {
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
} else {
stopifnot(!is.null(txdb))
}
Quality checks
P-values
Theoretically, the p-values should be uniformly distributed between 0 and 1.
This table shows the number of candidate Differentially Expressed Regions (DERs) with FWER adjusted p-values less or equal than some commonly used cutoff values.
This plot shows the density of the region lengths for all regions. The bottom panel is restricted to significant regions (q-value < 0.1)
Region Area
xrange <- range(log10(regions.df.plot$area[finite.area]))
if(inf.area > 0) {
print(paste('Dropping', inf.area, 'due to Inf values.'))
}
p3a <- ggplot(regions.df[finite.area, ], aes(x=log10(area), colour=seqnames)) +
geom_line(stat='density') + labs(title='Density of region areas') +
xlab('Region area (log10)') + scale_colour_discrete(limits=chrs) +
xlim(xrange) + theme(legend.title=element_blank())
p3b <- ggplot(regions.df.sig.area, aes(x=log10(area), colour=seqnames)) +
geom_line(stat='density') +
labs(title='Density of region areas (significant only)') +
xlab('Region area (log10)') + scale_colour_discrete(limits=chrs) +
xlim(xrange) + theme(legend.title=element_blank())
grid.arrange(p3a, p3b)
This plot shows the density of the region areas for all regions. The bottom panel is restricted to significant regions (q-value < 0.1)
Null regions: width and area
p4 <- ggplot(nulls.df, aes(x=log10(width), colour=chr)) +
geom_line(stat='density') + labs(title='Density of null region lengths') +
xlab('Region width (log10)') + scale_colour_discrete(limits=chrs) +
theme(legend.title=element_blank())
nulls.inf <- !is.finite(nulls.df$area)
if(sum(nulls.inf) > 0) {
print(paste('Dropping', sum(nulls.inf), 'due to Inf values.'))
}
p5 <- ggplot(nulls.df[!nulls.inf, ], aes(x=log10(area), colour=chr)) +
geom_line(stat='density') + labs(title='Density of null region areas') +
xlab('Region area (log10)') + scale_colour_discrete(limits=chrs) +
theme(legend.title=element_blank())
grid.arrange(p4, p5)
This plot shows the density of the null region lengths and areas. There were a total of 4032470 null regions.
Mean coverage
xrange <- range(log2(regions.df.plot$meanCoverage))
p6a <- ggplot(regions.df.plot, aes(x=log2(meanCoverage), colour=seqnames)) +
geom_line(stat='density') + labs(title='Density of region mean coverage') +
xlab('Region mean coverage (log2)') + scale_colour_discrete(limits=chrs) +
xlim(xrange) + theme(legend.title=element_blank())
p6b <- ggplot(regions.df.sig, aes(x=log2(meanCoverage), colour=seqnames)) +
geom_line(stat='density') +
labs(title='Density of region mean coverage (significant only)') +
xlab('Region mean coverage (log2)') + scale_colour_discrete(limits=chrs) +
xlim(xrange) + theme(legend.title=element_blank())
grid.arrange(p6a, p6b)
This plot shows the density of the region mean coverage for all regions. The bottom panel is restricted to significant regions (q-value < 0.1)
Mean coverage vs fold change
The following plots are MA-style plots comparing each group vs the first one. The mean coverage is calculated using only two groups at a time and is weighted according to the number of samples on each group. Note that the mean coverage and fold change as calculated here do not taking into account the library sizes.
These plots are only shown when there are two or more groups. A total of 5 plot(s) were made.
for(j in grep('log2FoldChange', colnames(values(fullRegions)))) {
## Identify the groups
groups <- strsplit(gsub('log2FoldChange', '',
colnames(values(fullRegions))[j]), 'vs')[[1]]
## Calculate the mean coverage only using the 2 groups in question
j.mean <- which(colnames(values(fullRegions)) %in% paste0('mean', groups))
groups.n <- sapply(groups, function(x) { sum(optionsStats$groupInfo == x) })
ma.mean.mat <- as.matrix(values(fullRegions)[, j.mean])
## Weighted means
ma.mean <- drop(ma.mean.mat %*% groups.n) / sum(groups.n) +
optionsStats$scalefac
ma.fold2 <- drop(log2(ma.mean.mat + optionsStats$scalefac) %*% c(1, -1))
ma <- data.frame(mean=ma.mean, log2FoldChange=ma.fold2)
ma2 <- ma[is.finite(ma$log2FoldChange), ]
fold.mean <- data.frame(foldMean=mean(ma2$log2FoldChange, na.rm=TRUE))
p.ma <- ggplot(ma, aes(x=log2(mean), y=log2FoldChange)) +
geom_point(size=1.5, alpha=1/5) +
ylab("Fold Change [log2(x + sf)]\nRed dashed line at mean; blue line is GAM fit: y ~ s(x, bs = 'cs')") +
xlab(paste('Mean coverage [log2(x + sf)] using only groups', groups[1], 'and',
groups[2])) + labs(title=paste('MA style plot:', groups[1], 'vs ',
groups[2])) + geom_hline(aes(yintercept=foldMean), data=fold.mean,
colour='#990000', linetype='dashed') +
geom_smooth(aes(y=log2FoldChange, x=log2(mean)), data=subset(ma2,
mean > 0), method = 'gam', formula = y ~ s(x, bs = 'cs'))
print(p.ma)
}
